Accurately accounting for sizing uncertainty in inspection

ABSTRACT

In one embodiment, a method implemented by a processor that receives plural sets of values corresponding to plural matched pairs of anomalies from a first inspection and second inspection following the first inspection, a first portion of each pair corresponding to the first inspection and a second portion of each pair corresponding to the second inspection, the plural sets of values corresponding to wall loss information for plural locations of a fluid carrying vessel; computes first and second statistical descriptions of a respective accuracy of the first and second inspections; and computes a revised estimate of the plural sets of values based on the first and second statistical descriptions.

TECHNICAL FIELD

This disclosure relates in general to fluid carrying systems, and moreparticularly, to integrity assessment of fluid carrying systems.

DESCRIPTION OF THE RELATED ART

Fluid carrying systems may include many components, such as transportpipelines for gas or liquid transmission, refinery piping, heatexchangers, pressure vessels, valves, etc. Such systems may be locatedin facilities on land, or harder to reach areas, such as off-shorefacilities, or in the case of pipelines, in between facilities as amechanism for transporting gas or liquids over a distance. Over time,components of fluid carrying systems deteriorate, which may compromisethe integrity of the fluid carrying system. For instance, corrosion mayreduce the wall thickness of heat exchangers, pipes, or pressure vesselsto such an extent that leaks or bursts occur, resulting in safety and/orfinancial consequences that prudent organizations make efforts to avoid.Such efforts include scheduled maintenance, which when done properly,may mitigate the risk of catastrophic failure despite the cost ofmaintenance downtime. However, if scheduled maintenance is toopremature, unnecessary costs may be imposed, a situation exasperated inless accessible locales, such as subsea infrastructures.

SUMMARY

In one embodiment, a method implemented by a processor that receivesplural sets of values corresponding to plural matched pairs of anomaliesfrom a first inspection and second inspection following the firstinspection, a first portion of each pair corresponding to the firstinspection and a second portion of each pair corresponding to the secondinspection, the plural sets of values corresponding to wall lossinformation for plural locations of a fluid carrying vessel; computesfirst and second statistical descriptions of a respective accuracy ofthe first and second inspections; and computes a revised estimate of theplural sets of values based on the first and second statisticaldescriptions.

BRIEF DESCRIPTION OF THE DRAWINGS

The systems and methods described herein can be better understood withreference to the following drawings. The components in the drawings arenot necessarily drawn to scale, emphasis instead being placed uponclearly illustrating the principles of the present disclosure. In thedrawings, like reference numerals designate corresponding partsthroughout the several views.

FIG. 1 is a schematic diagram of an example fluid carrying system forwhich embodiments of integrity assessment systems and methods may beemployed.

FIG. 2 is a schematic diagram of an example inspection report for apartial inspection of a heat exchanger that is illustrated incross-section with various symbols conveying the wall loss condition ofthe respective tubes.

FIG. 3 is a block diagram of an embodiment of an example integrityassessment (IA) system embodied as a computing device.

FIG. 4 is a screen diagram of an embodiment of an example graphics userinterface (GUI) that enables input of limit states, among otherinformation.

FIG. 5 is a screen diagram of an embodiment of an example GUI thatenables input of values corresponding to maximum inspected anomaliesfound in tubes among plural zones of a heat exchanger.

FIG. 6 is a screen diagram of an embodiment of an example output graphicthat provides an illustration of a likelihood of a leak estimate over acontinuum of time among plural zones of a heat exchanger under ano-action scenario.

FIG. 7 is a screen diagram of an embodiment of an example output graphicthat provides an illustration of a likelihood of a leak estimate over acontinuum of time among plural zones of a heat exchanger under asimulated full inspection scenario.

FIG. 8 is a screen diagram of an embodiment of an example output graphicthat provides an illustration of a likelihood of a leak estimate over acontinuum of time among plural zones of a heat exchanger based ondifferent scenarios.

FIG. 9 is a graphic that illustrates uncertainty in measurement ofanomalies in a fluid carrying vessel and an example mechanism to providea more accurate assessment of anomaly sizes and corrosion rates.

FIGS. 10-13 are flow diagrams that illustrate example method embodimentsfor estimating leak likelihoods in a fluid carrying vessel.

FIGS. 14-16 are flow diagrams that illustrate example method embodimentsfor providing a more accurate estimate of anomaly sizes and/or corrosionrates associated with the anomalies.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Disclosed herein are integrity assessment (IA) system and methodembodiments (herein, collectively referred to also as an IA system or IAsystems) that identify anomalies corresponding to metal loss orotherwise irregular reduction in metal thickness (e.g., caused byerosion, corrosion, and/or other mechanisms causing metal loss) of afluid carrying vessel (e.g., heat exchanger, pipe, pressure vessel,etc.) that are likely to leak over time, enabling an assessment of theremaining life of the fluid carrying vessel. For instance, for somefluid carrying vessels, such as those embodied as a heat exchanger, itis industry practice to inspect a portion (e.g., 10%) of the entire heatexchanger in what is also referred to as a partial inspection. However,a question may arise as to the condition of the uninspected tubes of theheat exchanger; as such tubes may be a source of the next leak. Further,corrosion in heat exchangers is a stochastic process, and hence anaccurate assessment of the likelihood of a leak over a time continuum isdesired. Additionally, there is considerable uncertainty in the measuredvalues, varying depending on the material of the vessel, the inspectionmethod, among other factors. Certain embodiments of IA systems addressone or more of these issues and hence facilitate a more accurate andjudicious approach to integrity management. In one embodiment, an IAsystem provides one or more graphics user interfaces (GUIs) that enablethe receipt of predefined limit states corresponding to plural risklevels and the receipt of values corresponding to maximum-sizedanomalies, such anomalies identified in one or more zones of the fluidcarrying vessel via nondestructive inspection techniques. Equipped withthis information, the IA system computes an estimate of the likelihoodof a leak at inspected and uninspected locations, enabling an assessmentof remaining heat exchanger life and properly planned maintenance.

For some fluid carrying vessels, such as pipelines, there may be 100%inspection employed, yet there is uncertainty in the measurementaccuracy. A similar concern applies to inspections of other fluidcarrying vessels, such as heat exchangers, where there may beuncertainty in the accuracy corresponding to the chosen method ofinspection. Accordingly, also disclosed are certain IA systemembodiments that match each anomaly among plural anomalies identifiedover plural inspections spaced in time, record values corresponding tothe size of the anomaly (e.g., depth, such as wall loss), model theaccuracy of each inspection iteration, and compute a revised estimate ofthe values based on the modeled accuracy of the inspections, and in someembodiments, estimate the growth rate (e.g., corrosion rate, erosionrate, and/or rate of metal loss) of each anomaly based on the revisedestimated values. Digressing briefly, it is known that inspectionefforts typically highlight those anomalies of maximum size (e.g.,deepest depths), but the sizes of those anomalies tend to beoverestimated. One result of overestimation is that anomalies identifiedpreliminarily as having the largest size may not actually be thelargest, but rather, may possess comparatively smaller anomalies withlarger error terms; potentially causing more serious anomalies to bemissed and/or premature and costly scheduled maintenance. Certainembodiments of IA systems address these and/or other features, asexplained further below.

In general, more accurate estimates of the actual anomaly sizes and theassociated growth rates (e.g., corrosion rates) increase the accuracy ofthe remaining life predictions. Additionally, more accurate estimates ofthe remaining life allow organizations to make better integritymanagement decisions. For instance, economic value may be generatedthrough the deferment of unnecessary repairs or re-inspections, andre-allocation of inspection resources on heat exchangers or other fluidcarrying vessels with highest risk, therefore reducing risk ofunexpected leaks in other bundles or vessels. Certain embodiments of IAsystems also help with optimal allocation of inspection (e.g., where,when and how much). For instance, during refinery turnarounds inspectionmay be the critical path. More optimal inspection may reduce theshutdown time and provide added value.

These advantages and/or features, among others, are describedhereinafter in the context of an IA system embodied as a computingdevice, the computing device used in some embodiments to assessanomalies in tubes of a fluid carrying vessel embodied as a heatexchanger, and in some embodiments, to address inaccuracies in inlineinspection methods applied to a pipeline. It should be understood thatthe selection of certain fluid carrying vessels are used forillustration only, and not intended as a limitation in the applicationof certain embodiments of IA systems and methods. For instance, fluidcarrying vessels, such as piping (e.g., refinery or facility piping),pipelines (e.g., trunk or transmission), pressure vessels, heatexchanger tubing, etc., may benefit from the anomaly assessment (e.g.,even if partial inspection is not employed), or inaccuracy assessment(e.g., even if not specific to a pipeline) of certain embodiments of theIA systems and methods and hence are contemplated to be within the scopeof the disclosure. Further, it should be understood by one havingordinary skill in the art that, though specifics for one or moreembodiments are disclosed herein, such specifics as described are notnecessarily part of every embodiment.

Attention is directed to an example environment in which one or moretypes of fluid carrying vessels may reside, and for which certainembodiments of IA systems may be applied. In particular, FIG. 1 is anexample fluid carrying system 100 that may reside at an industrialfacility, such as a petrochemical plant. Although depicted as aland-based facility, other fluid carrying systems contemplated withinthe scope of the present disclosure may be located off-shore or outsideof production facilities. The fluid carrying system 100 comprises anetwork of components used to transport, store, and/or process liquidand/or gas. Such components include one or more heat exchangers 102,piping 104, and pressure vessels 106 (heat exchangers 102, piping 104,and pressure vessels 106 collectively, and individually, also referredto as a fluid carrying vessel). The heat exchanger 102, piping 104, andpressure vessel 106 may be constructed of one of a plurality ofdifferent types of materials, including carbon steel, stainless steel,galvanized steel, among other materials. As is known, the heat exchanger102 includes a plurality of individual tubes that may be constructed ofcarbon steel, brass, stainless steel, among other materials.

The various components of the fluid carrying system 100 are subject toinspection and maintenance as part of an overall integrity managementstrategy employed by owners or operators of the fluid carrying system100. For instance, with regard to the heat exchangers 102, regularinspection may be imposed using one or more of a plurality of differentinspection techniques. Examples of inspection techniques includenondestructive techniques based on remote field eddy current,ultrasound, among others. In some embodiments, destructive testing mayalso be employed at least in part. Anomalies on the various tubes of aheat exchanger 102 are identified through these inspection techniques,the anomalies identified along points or locations of the tubing wherethere is evidence of, for instance, wall loss, with values correspondingto their (the anomaly's) respective measured sizes (e.g., depth).However, one shortcoming of such inspections is that the measurementsare subject to varying degrees of sizing uncertainty. For instance, areport generated responsive to such an inspection of the heat exchanger102 may reveal a wall loss of 30%, when in reality, the extent of wallloss is much less (or greater). Certain embodiments of IA systems may beused to provide a more accurate assessment of the extent of wall loss,enabling prediction of the remaining useful life of the heat exchanger,including an identification of which tubes are presently in need ofrepair or plugging, and an estimate of when tubes are likely to leak.

FIG. 2 is an example report 200 that may be generated in response to anondestructive, partial inspection of the heat exchanger 102, andillustrates another possible shortcoming of an overall integritymanagement strategy that certain embodiments of IA systems address. Theheat exchanger 102 comprises a top half 202 and a bottom half 204 oftubes. Each tube, such as tube 206, is represented by a circle, andshown are different types of circles representing different conditionsof the tubes. For instance, a circle with a diagonal line through itrepresents a tube without detectable anomalies, whereas a clear (e.g.,empty) circle symbolizes a tube that has not been tested. A circle thatenvelopes a number conveys to the user that the associated tube has adefined percentage of measured wall loss, as indicated by the associatedlegend key. For instance, a circle with the number “1” within itrepresents a tube where wall loss falls within 5-9%, and a circle withthe number “2” within it represents a tube where wall loss falls within10-19%. As the numbers within a given circle increase, the wall losspercentage increases. It is also noted that circles where half of theinterior is darkened symbolizes obstructed tubes.

Based on the extensive quantity of clear circles in the top half 202, itis clear that the heat exchanger 102 has only been partially tested. Inpractice, especially for heat exchangers 102 with large bundles oftubes, only a small percentage of the total number of tubes may beinspected. However, one possible shortcoming of this type of strategy isthat the tube with the worst wall loss may not have been inspected.Certain embodiments of IA systems compute the reliability of the bundle,with due consideration for both inspected and uninspected tubes.Further, certain embodiments of IA systems simulate the potentialbenefit of performing a full inspection and/or repair, with furtherconsideration of obstructed tubes and plugged tubes; enabling remaininglife computations based on the aforementioned scenarios and anidentification of tubes likely in need of replacement.

Having described certain features and/or advantages of employing IAsystems as an integral part of a facility's integrity managementstrategy, attention is directed to FIG. 3, which is a block diagram ofone example embodiment of an IA system embodied as a computing device300. It should be understood that an IA system may be embodied withfewer or some different components, such as the logic (e.g., softwarecode) stored in memory and a processor that executes the logic in someembodiments, or the software logic encoded on a computer readable mediumin other embodiments. In some embodiments, the IA system may encompassthe computing device 300 and additional components, such as modularinspection systems or remotely located storage devices (e.g., accessedover a network) that comprise data structures, such data structuresstoring historical data corresponding to manually tested tubes,historical growth rates, among other data.

The computing device 300 contains a number of components that arewell-known in the computer arts, including a processor 302, memory 304,a network interface 314, and a peripheral I/O interface 316. In someembodiments, the network interface 314 enables communications over alocal area network (LAN) or a wide area network (WAN). In someembodiments, the network interface 314 enables communication over aradio frequency (RF) and/or optical fiber network. The peripheral I/Ointerface 316 provides for input and output signals, for example, userinputs from a mouse or keyboard, and outputs for connections to aprinter or display device (e.g., computer monitor). The computing device300 further comprises connections to a storage device 310 (e.g.,non-volatile memory or a disk drive). For instance, the storage device310 may comprise one or more database, such as database 312, for storingproprietary data on heat exchanger tubes, historical growth rates,and/or data on other fluid carrying vessels. Databases 312 may be basedon any number of known database management systems (DBMS), includinghierarchical databases, network databases, relational databases, andobject oriented databases. For instance, suitable DBMS are widelyavailable and include ORACLE, SYBASE, MICROSOFT SQL Server, and DB2. Onesuitable database system is a relational database based on SQL language.The data processing is handled by a database engine (not shown) and canbe accessed by various searching means, including Boolean logicevaluation, proximity calculations, and fuzzy logic evaluation. Theaforementioned components are coupled via one or more busses 318.Omitted from FIG. 3 are a number of conventional components that areunnecessary to explain the operation of the computing device 300.

In one embodiment, the IA system, or components thereof, is embodied assoftware and/or firmware (e.g., executable instructions) encoded on atangible (e.g., non-transitory) computer readable medium such as memory304 or the storage device medium (e.g., CD, DVD, among others) andexecuted by the processor 302. For instance, in one embodiment, thesoftware (e.g., software logic or simply logic) includes graphics userinterface (GUI) logic 306 and algorithm (“algo”) compute logic 308. TheGUI logic 306 provides for the display of a GUI that enables the receiptof user information, and/or generates output graphics (or simply,graphics or visualizations) that represent an estimate of the likelihoodof a leak in one or more tubes among various zones of a heat exchanger.In one embodiment, the GUI logic 306 is EXCEL-based. The computerreadable medium may include technology based on electronic, magnetic,optical, electromagnetic, infrared, or semiconductor technology.

The algo compute logic 308 may comprise functionality (e.g., executablecode) to cause the processor 302 to compute algorithms that are stored,for instance, in a dynamic link library (DLL) in memory 304 or thestorage device 310. For instance, in one embodiment, the algo computelogic 308 comprises an EXCEL-based computation engine (with macros)comprising binary code compiled in DLL format that enables directcommunication (e.g., without an intermediate file) between the libraryand EXCEL. In general, the algo compute logic 308, when executed by theprocessor 302, computes an estimate of the annual likelihood of a leakover time with consideration to various limit states (described below).When these limits are exceeded (e.g., safety and/or financialreliability thresholds, though others may also be included in someembodiments), the user is alerted that action must be taken. In oneembodiment, the algo compute logic 308 calculates reliability of theheat exchanger bundle as-is, while considering (a) the effect of partialinspection (e.g., it is possible that a partial inspection did notinspect the tube with worst wall loss), (b) the effect of sizinguncertainty (e.g., it is possible that the deepest reported wall lossover or underestimates the true maximum wall loss), (c) the wall lossrate uncertainty (e.g., future wall loss may occur at a different ratethan observed in the past), and/or (d) variation in wall thickness(e.g., tube thickness is variable). The algo compute logic 308 alsofactors in the effect of repair criterion by computing the effect onremaining life, assuming all tubes that exceed a user-defined wall losspercentage are plugged or replaced.

In addition, the algo compute logic 308 simulates the maximum potentialbenefit of inspection and repair. For instance, if only a partialinspection is performed, the algo compute logic 308 can simulate thebenefit that might be gained from performing a full inspection andsubsequent repair, while considering the role of obstructed and pluggedtubes. To this extent, the algo compute logic 308 reports both theremaining life under such a scenario as well as the number of tubes thatlikely have to be replaced.

With regard to the sizing uncertainty, in one embodiment, the algocompute logic 308 accounts for the sizing uncertainty using data that iscompiled from inspector testing results, wherein the inspectors arequalified (e.g., certified) according to certain detection and sizingaccuracy protocols or procedures. The algo compute logic 308 also isresponsible for anomaly matching and growth rate (e.g., corrosion rate)computation, as explained further below. Note that functionality of thealgo compute logic 308 may be further distributed among separate butcooperating software modules.

In some embodiments, functionality associated with one or more of thevarious components of the IA system may be implemented in hardwarelogic. Hardware implementations include, but are not limited to, aprogrammable logic device (PLD), a programmable gate array (PGA), afield programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), a system on chip (SoC), and a system in package (SiP).In some embodiments, functionality associated with one or more of thevarious components of the IA system may be implemented as a combinationof hardware logic and processor-executable instructions (software and/orfirmware logic). It should be understood by one having ordinary skill inthe art, in the context of the present disclosure, that in someembodiments, one or more components of the IA system may be distributedamong several devices, co-located or located remote from each other.

FIG. 4 is a screen diagram of an example graphics user interface (GUI)400 prompted by the IA system to enable user access to the functionalityof the IA system. In some embodiments, the GUI 400 may be prompted inresponse to successful completion of a prior-displayed security screen(not shown) in which the user may be required to provide informationsuch as name and/or one or more passwords or other information to gainaccess to the IA system. It should be understood by one having ordinaryskill in the art, in the context of the present disclosure, that the GUI400 shown in FIG. 4 is merely illustrative, and should not be construedas implying any limitations upon the scope of the disclosure. Forinstance, the GUI 400 may include fewer or additional choices, and/or adifferent arrangement of GUI features in a single GUI or dispersed amonga plurality of GUIs. In one embodiment, the GUI 400 comprises aninspection method section 402, a limit state section 404, and pluraloption button icons, including a show risk matrix button icon 406, agenerate input sheet button icon 408, and a cancel button icon 410.

The inspection method section 402 comprises an inspection method scrollbox 412, number of inspection zones scroll box 414, and time period ofinterest scroll box 416. By selecting the up or down arrow icon of agiven box 412, 414, or 416, the user may incrementally scroll through aplurality of predefined choices. Note that reference to scroll boxeshereinafter contemplates a similar scroll functionality and manner ofoperation. Note that in some embodiments, other mechanisms for providingpredefined choices in place of one or more of the scroll boxes 412, 414,and 416 are contemplated to be within the scope of the disclosedembodiments, such as drop-down menus, among others.

The inspection method scroll box 412 comprises predefined choices that auser may select to identify the inspection method (e.g., nondestructivemethod) used as a basis for obtaining wall thickness data (e.g., to beentered in another GUI). For instance, remote field—carbon steel, andeddy current—brass eddy current—Inco are shown as example choices, withthe understanding that a user may scroll through other choices forinspection methods, including such methods as eddy current-stainless,eddy current-duplex steel, ultrasound, radiography, among other knowninspection methods.

The number of inspection zones scroll box 414 enables a user to selectthe number of zones subject to inspection. Digressing briefly, there arevarious reasons one may choose to break-up a heat exchanger 102 intoplural zones. For instance, one reason may be that there has been apartial retubing of the heat exchanger 102 in the past. A new zoneshould be defined for the retubed section of the exchanger 102 since ithas a different age than the remainder of the exchanger. Failure todefine a new zone may result in incorrect results. Another reason todifferentiate the heat exchanger 102 into different zones is becausedifferent inspection coverage may be applied to various parts of theexchanger. For instance, if the inlet area has 100% coverage, but theremainder of the heat exchanger 102 has 20% inspection coverage,separate zones should be defined so that each zone has a uniformcoverage. An additional reason for designating different zones isbecause a reported tube sheet summary may clearly indicate that the wallloss is much more advanced in a certain region of the heat exchanger 102than in others. By breaking up the exchanger 102 into multiple zones,more accurate results may be produced. For instance, although all tubesare the same age and uniform coverage exists throughout the bundle,physical evidence may suggest that different mechanisms are at work indifferent parts of the heat exchanger 102. Breaking up the heatexchanger 102 into multiple zones may enable an integrity managementstrategy where only a part of the exchanger is inspected or retubed. Inat least one embodiment, each zone contains at least 20 inspected tubes,and if a zone contains significantly less than that, some zones may bemerged. Note that a single zone may also be selected for purposes of IAsystem analysis.

The time period of interest scroll box 416 enables the user to selecthow far into the future the predictions should be made. Typicalselections of time period or horizon are one or two regular turnaroundintervals, which in some implementations, may be manifested as 5-10years or 7-14 years. In the example shown in FIG. 4, selection of thevalue “7” refers to seven (7) years from the date of inspection. Forinstance, plant inspection records may be reviewed a period of timebefore the seven years expires (e.g., five (5) years from the date ofinspection) to assess whether the planned shutdown is feasible withoutrisk of leakage, hence avoiding the costs of an unexpected shutdown.

With regard to the limit state section 404, the user may selectcheckboxes 418 and/or 420 corresponding respectively to asafety/health/environment limit state (herein, also collectivelyreferred to as a safety limit state), and assets or financial limitstates (e.g., damage, lost product). Note that in some embodiments, thetwo categories corresponding to checkboxes 418 and 420 may be furtherdelineated with respective checkboxes, or replaced by other constraintsin some embodiments. In one embodiment, the checkbox 418 isautomatically checked as a default, and the user may optionally selectthe assets checkbox 420 to add the asset constraint. Associated with thesafety limit state category are a consequence scroll box 422 and atarget risk level scroll box 424. Similarly, associated with the assetslimit state category are a consequence scroll box 426 and target risklevel scroll box 428. Using the values selected from scroll boxes 422,424, 426, and 428, the algo compute logic 308 computes an acceptable ortarget likelihood of an undesirable event based on the severity of theconsequence and maximum accepted risk level. In some embodiments, thelevels in scroll boxes 422, 424, 426, and 428 may default tonon-actionable limit states (e.g., where there is no triggering ofoperator or user intervention). Guidance on which limit state levelstrigger operator intervention and the meaning of the various levels maybe ascertained via selection of the show risk matrix button icon 406. Insome embodiments, entry of incompatible levels may prompt a warningbarker or pop-up that advises the user to re-enter different values, orin some embodiments, cause automatic entry of levels that represent acompromise to the intent of the user entries with or without appropriatewarning to the user. Although levels as high as seven (7) are inferredfrom the values shown in boxes 424 and 428, it should be understood thatdifferent quantities of levels may be used in some embodiments, withrelatively the same, or different, underlying meanings. In general, anyone of a number of risk matrices may be used, for instance tailored tothe goals associated with a given facility or organization.

Responsive to selecting the user sheet button icon 408, GUI 500 isprovided for display on a display device coupled (via wired wirelessconnection) to the computing device 300. It should be understood by onehaving ordinary skill in the art, in the context of the presentdisclosure, that the GUI 500 shown in FIG. 5 is merely illustrative, andshould not be construed as implying any limitations upon the scope ofthe disclosure. Note that values shown in FIG. 5 are illustrative only,and not intended to correlate identically with the heat exchanger 102 orthe GUI 400. For instance, though three zones are selected in FIG. 4,four zones are illustrated in FIG. 5 (whereas in practice, it should beapparent that selection of three zones in GUI 400 results in three zonesfor assessment in GUI 500). Other examples may be noted. For instance,the GUI 500 may include fewer or additional choices, and/or a differentarrangement of GUI features in a single GUI or dispersed among aplurality of GUIs. Further, note that in some embodiments, the GUIsdescribed herein are not limited to EXCEL-based GUIs, but rather, may beweb-based pages that enable remote access, such as through browsersoftware or GUI screens embedded in other software codes such asComputerized Maintenance Management Systems.

In one embodiment, the GUI 500 comprises an information summary box 502,safety and assets summary boxes 504 and 506, respectively, zone and tubesummary section 508, and instruction/activation box 510. In someembodiments, each of the constituent parts of the GUI 500 may bedistinguished by different colors or patterns. For instance, zone andtube summary section 508 may be separated into plural sections. Further,in some embodiments, one or more of the aforementioned components of theGUI 500 may be combined, or in some embodiments, further delineated intoseparate, distinguishable sections. The information summary box 502comprises information applicable to the entire heat exchanger 102,including the location, heat exchanger identification number, date ofinspection, nominal wall thickness of the tubes, a repair threshold, andtime period of interest. A user may insert extra rows in this box 502 ifhe or she wishes to record additional notes. In one embodiment, defaultvalues for the inspection date and repair threshold are provided, butusers are encouraged to check these values and make adjustments asnecessary. The repair threshold provides a defined delineated value,beyond which the tube is assumed, for purposes of computation, to betaken out of service (e.g., plugged or replaced). Herein, reference to“taking out of service” or plugging the tubes or the like is intended torefer to simulation by the IA system of the physical acts of taking thetubes out of service for purposes of computation, except where it isclear in the context of the description that an actual physical repairis taking place. In other words, any anomaly reported more severe thanthis threshold value is assumed to be removed. Increasing the repairthreshold decreases the number of plugs required, but it also increasesthe likelihood of a leak in the future. Therefore, this parameterenables a balancing of the likelihood of a leak and the number of tubesthat need to be plugged.

The safety and assets summary boxes 504 and 506 provide a record of thechoices that were made in GUI 400 regarding the limit states. Asindicated previously, these values may differ depending on theapplication or implemented risk matrix. The GUI 500, and in particular,safety and assets summary boxes 504 and 506, reflects which limit stateis checked (both safety and assets in this example, though one or theother 504, 506 may be selected in some implementations), what valueswere input for the accepted risk levels and consequence indices for eachlimit state category, as well as the resulting likelihood index for eachcomputed by the algo compute logic 308. As expressed above, thoughcertain definitions are shown in FIG. 5 underlying the values of theselected limit states, it should be understood that other risk matricesand their underlying definitions may be used in some embodiments.

The zone and tube summary section 508 comprises an area where users mayenter zone-specific information. For each zone, users input the totalnumber of tubes (e.g., the number of tubes in use, not plugged tubes) aswell the number of inspected tubes. Obstructed tubes are not counted asinspected tubes. Users also enter the age of the tubes in each zone,which is used to estimate wall loss rates. The zone and tube summarysection 508 also comprises a defect list. The dashed lines in thissection 508 symbolize that additional values may be added to the list.Users may import (e.g., paste, file transfer, etc.) the maximum anomaly(e.g., indicated in the section 508 as residing in a “defect list,”where the term “defect” is herein used interchangeably with anomalyexcept where their differences are clear from the context in whichdefect is described) found in each inspected tube within a given zone.This anomaly information may be entered as a percentage (on a 0 to 100scale) or as a fraction (on a 0 to 1 scale). Note that for inspectionreports that list the remaining wall thickness value (as in someultrasound-based third party reports), these values are first convertedto wall loss by the user. In some embodiments, such remaining wallthickness values may be automatically converted by the algo computelogic 308.

Note that in some embodiments, consistent with the caveat that otherexample GUIs and/or GUI arrangements are contemplated to be within thescope of the disclosure, a zone and tube summary section may be employedwhereby one section lists the zones at issue and another,distinguishable section comprises information electronically importedfrom an inspection report into the GUI 500 corresponding to theinspected tubes of the various zones at issue. For instance, in theaforementioned one section, one column of zone definitions (e.g., zone1, zone 2, etc.) may be employed with one or more additional columnsdedicated to an identification of the beginning and ending exchangertube row having information listed in the aforementioned other section.Further, the distinguishable section may comprise such information as anidentification of the tube, the row of the exchanger to which itbelongs, percent wall loss, a code corresponding to the condition (e.g.,pitted, obstructed, plugged, etc.), among other optional informationtypical to inspection reports.

The instruction/activation box 510 comprises instructions to a user forcompleting the GUI 500. In some embodiments, additional information maybe incorporated in this section 510, or in some embodiments, informationicons next to each section (which in some embodiments may replace theinformation in instruction/activation box 510) may be inserted andactivated by user selection (e.g., clicking on the icon by a mouse orother user input device) to enable a user to learn more about completinga given section. When the input is complete, the instruction/activationbox 510 is clicked (e.g., using a pointer device, such as a mouse, tosingle or double click) to cause the algo compute logic 308 to generatethe leak likelihood estimates. In one embodiment, though not intended tobe limiting but rather used as an example, the calculations may take upto fifteen (15) seconds per zone to complete, although actualcomputation times depend on the processing power of the computing device300. Computed results are added to an output chart (e.g., graphic) orother visualization on a per zone basis as they become available.

FIGS. 6-8 are example screen diagrams that provide an illustration ofvarious visualizations (e.g., output charts or output graphics, alsoreferred to herein as merely graphics) that the IA system generatesresponsive to the inputs entered in the GUIs 400 and 500. It should beunderstood by one having ordinary skill in the art, in the context ofthe present disclosure, that the output charts shown in FIGS. 6-8 aremerely illustrative, and should not be construed as implying anylimitations upon the scope of the disclosure.

For instance, in some embodiments, other visualizations may beimplemented, including visualizations in the form of tables, bar charts,among other mechanisms to facilitate an interpretation of the resultsand enable well-informed integrity management decisions. Referring toFIG. 6, shown is an output graphic 600 embodied as a chart with ahorizontal axis 602 indexed in years since inspection and a verticalaxis 604 indexed in likelihood (e.g., probability) of leak per year. Forinstance, and referring to the vertical axis 604, the index “1.E+0”corresponds to a 100% likelihood of a leak. The index immediatelybeneath index 1.E+0 is designated as “1.E-1,” and corresponds to a leaklikelihood of 10%. The index beneath 1.E-1 is index 1.E-2, whichcorresponds to a leak likelihood of 1%, and so on (0.1%, 0.01%, etc.) insimilar fashion as one progresses downward along the vertical axis 604.Also shown in FIG. 6 is a legend at the top of the output graphic 600,indicating by line format (e.g., dashed, broken line, etc.) what eachrespective line symbolizes or represents. In some embodiments, thelegend may indicate by color distinction what each respective line inthe output graphic 600 represents, or may distinguish via a combinationof both line format and color in some embodiments, among othermechanisms of distinction known in the art.

The output graphic 600 further includes horizontal lines 606 and 608representing safety and asset limit states, respectively, and computedestimate lines 610, 612, 614, and 616 representing tubes in zones 2, 3,1, and total (i.e., all zones). The horizontal lines 606 and 608 arecomputed, targeted likelihood estimates based on entries made in the GUI400. As time progresses, the tube wall (or pipe wall in someimplementations) thins due to corrosion (and/or other wall lossmechanisms) and hence the curved lines 610, 612, 614, and 616 indicatehow the likelihood of a leak increases over time. In general, the outputgraphic 600 displays the likelihood of a leak occurring in each year andcompares this result against the target likelihood based on safety andasset consequences of a heat exchanger leak if no action is taken andthe past process conditions remain essentially unchanged. For instance,in FIG. 6, the output graphic 600 indicates that at the time ofinspection, the likelihood of a leak exceeds the target likelihoods andtherefore immediate action is required; some tubes either need to beplugged or replaced. In one embodiment, the contribution of eachindividual zone to the total likelihood of leak is not shown by defaultbut can be added to the summary chart if desired through a macrofunction.

More specifically, the output graphic 600 illustrates the logicalpartitioning of a heat exchanger 102 into three (3) separate zones,zones 1, 2, and 3, as represented by curved lines 614, 610, and 612,respectively. Further, the output graphic 600 also comprises anall-zones or total line 616, which represents the contribution, from allof the respective zones 1, 2, and 3, to an overall leak likelihoodestimate. For instance, as revealed by the output graphic 600, zone 2(represented by line 610) appears to have tubes with the least amount ofwall loss, as it has the lowest estimated likelihood of a leak among thethree zones. At five (5) years, the estimated likelihood of a leak is at1.E-4 (e.g., 0.01%). At ten (10) years, the estimated likelihood of aleak for tubes of zone 2 is above 0.1%, and surpasses the targetedlikelihood 608 (e.g., critical threshold for assets), which enables auser to assess what actions may need to be taken to reduce this risk,such as via plugging or repairing one or more tubes, etc. Note that thesecond critical threshold represented by line 606 (corresponding tosafety) is surpassed by zone 2 after fifteen (15) years. A similarassessment for the other zones, and the total line, may be applied asdescribed above. As indicated above, the output graphic 600 reveals thatthe total exceeds both thresholds 606 and 608 at the time of inspection,and hence some action is required, as further described in associationwith FIG. 7 below.

Referring to FIG. 7, shown is an example output graphic 700 embodied asa chart with a horizontal axis 702 indexed in years since inspection anda vertical axis 704 indexed in likelihood of leak per year (based on asimulation of 100% inspection and plugging when the repair threshold hasbeen exceeded). The output graphic 700 further includes horizontal lines706 and 708 representing safety and asset limit states, respectively,and computed estimate lines 710, 712, 714, and 716 representing tubes inzones 1, 2, 3, and total (i.e., all zones). The manner of interpretingthe output graphic 700 is similar to the example given in associationwith the output graphic 600 of FIG. 6, and hence description of the sameis omitted here for brevity. The output graphic 700 further includes asub-chart 718 that indicates the likely number of plugged tubes per zoneand in total, and the percentage of tubes to plug or replace. Ingeneral, the output graphic 700 illustrates the results of what leakreduction performance might be achieved if a full inspection wereperformed and if all tubes with wall loss that exceeds the repairthreshold are taken out of service (either plugged or replaced). Forinstance, the sub-chart 718 in FIG. 7 indicates that if 112 tubes areplugged (e.g., about 7% of all tubes), a remaining safe life up to about7 to 8 years can be expected (e.g., compared to the output graphic 600,where the total 616 starts above the governing threshold represented byline 608). It is also assumed that the past process conditions remainessentially unchanged; i.e., that past corrosion trends continue (albeitwith some uncertainty). For the uninspected tubes, the IA system (e.g.,algo compute logic 308 of the IA system) calculates the risk accordingto the wall loss distribution observed in a particular zone. The IAsystem also estimates the number of tubes that need to be plugged orrepaired. If the number of tubes that need to be plugged or repairedexceeds 10% of the total number of tubes in the zone, this situation isflagged in the sub-chart 718 (e.g., 11% under zone 3 may be visuallydistinguished, such as via red highlighting, bolder border, among othercolors or alert mechanisms). The contribution of each individual zone tothe total likelihood of leak may be added to the chart 702 as desired.

For instance, whereas lines 612, 614, and 616 exceeded at least thetargeted threshold represented by line 608 in the output graphic 600(FIG. 6) at year 5, judicious selection of tubes to plug or repair asindicated in the sub-chart 718 enables a lowering of these lines(corresponding to lines 712, 714, and 716) below the same assetthreshold (represented by line 708), since the risk of leak is mitigatedby the absence of flow through a blocked or repaired tube. In someimplementations, given the speed at which the IA systems compute theleak likelihood estimates, a dialogue between an operations person andthe user (e.g., engineer or technician) is facilitated by the use of theIA system and this output graphic 700, whereby various iterations ofplugging and/or repair scenarios may be investigated with this visualaid for different zones to achieve mutual goals of reducing risk whilealso maintaining efficiency in operations.

FIG. 8 illustrates another example output graphic 800 that provides (ona single chart) a comparison of three scenarios—no action (notrepresented by an estimated line in FIG. 8, but rather, coinciding withline 812), as inspected, and full inspection. The output graphic 800 isembodied as a chart with a horizontal axis 802 indexed in years sinceinspection and a vertical axis 804 indexed in likelihood of leak peryear (based on a simulation of 100% inspection and plugging when therepair threshold has been exceeded). The output graphic 800 furtherincludes horizontal lines 806 and 808 representing safety and assetlimit states, respectively, and computed estimate lines 810 and 812representing full inspection and as inspected scenarios, respectively.The drop down box 814 enables a user to select either the entireexchanger or a zone for which to compare the results. Note that the asinspected scenario corresponds to a display of the likelihood of a leakoccurring in each year, and includes a comparison of the result againstthe target likelihood based on safety and asset consequences of a heatexchanger leak if all inspected tubes with wall loss that exceeds therepair threshold are taken out of service (either plugged or replaced).It is also assumed that the past process conditions remain essentiallyunchanged. For zones with partial inspection (either intentionally—sayinspecting only 10 or 20% of the tubes—or due to the presence ofobstructed tubes), the IA system (e.g., algo compute logic 308 asexecuted by the processor 302) calculates the risk associated with theuninspected tubes.

If a full inspection is performed, the “As Inspected” and “FullInspection” scenarios lead to identical results. However, in thepractical case where some tubes are obstructed or intentionally notinspected (due to selection of partial inspection level of—say—10% or20%), these results may be very different. In one embodiment, whenreasonably deep anomalies are found during the inspection, the “asinspected” result only incorporates the effect of plugging or replacingtubes that were actually inspected. It is however quite likely that theuninspected portion of the heat exchanger 102 also contains tubes withdeep wall loss. The “as inspected” scenario takes this possibility (andthe associated risk) into consideration and that explains why “asinspection” results look very similar to “No Action” results and notlike “Full Inspection results” if deep anomalies are present. Unless afull inspection is performed, one cannot determine which tubes need tobe plugged.

Returning to an explanation of the output graphic 800, in general, thisgraphic 800 may be created for the heat exchanger 102 as a whole or foreach individual zone. The chart allows a user to quickly assess: (a) ifrunning the heat exchanger 102 without action is acceptable; (b) howeffective the current inspection is (e.g., for a partial inspection, itis possible that plugging tubes is ineffective because of the potentialthat other, uninspected, tubes may also have large wall loss); and (c)how effective a full inspection would be and how extensive a repairwould be necessary, which helps to make a decision whether moreinspection should be attempted or the heat exchanger 102 should becompletely retubed.

By default, the IA system (e.g., GUI logic 306 in cooperation with thealgo compute logic 308) plots the failure probability (i.e., a differentperspective in the manner of assessing the output graphics) of theentire heat exchanger 102 as function of time so the users may comparethe predicted reliability against the targets associated with each ofthe limit states. The failure probability is given by the probabilitythat the largest anomaly exceeds the critical wall loss value. Users mayclick on sections 504 and/or 506 to add exceedance probabilities forindividual zones. In one embodiment, the exceedance probability for theentire heat exchanger 102 should remain below the targets. Individualzone exceedance probabilities may help to identify which zones are thegreatest contributors to the overall risk.

Note that some embodiments of IA systems may employ output graphicswhere the risk level is associated with the vertical axis (e.g., titled,“reliability threat prioritization number”) with different values thanthose presented above, and limit states are shown as merely inspect andrepair alarm levels or thresholds (e.g., lines running horizontal). The“inspect” line may be lower (e.g., the governing line) on the chart thanthe repair line, and estimates falling between these lines are revealedas falling in a sort of “warning” zone that repair needs may beforthcoming. If the risk exceeds these respective repair or inspectlines, an inspection is triggered or a likely repair (e.g., triggers aplanning or pre-ordering strategy) is indicated.

Having described the IA system in the context of accurately predictingthe remaining life of a heat exchanger 102, attention is directed toaccurately accounting for sizing uncertainty in inspections. Digressingbriefly, and in general, sizing accuracy is finite, and some inspectiontools are more accurate than others. But generally speaking, sizinginaccuracy of non-intrusive inspection is sufficient to warrant explicittreatment. For pipelines, anomalies are typically sized by in-lineinspection instruments within 10%. Analysis of inspection results hasshown the results of validation efforts. Internal, proprietary dataindicates that the 80% confidence bounds for the sizing accuracy of sometechniques may be as accurate as ±5% or as inaccurate at ±20%, dependingon the technique that is used. For instance, for vessels configured aspipelines, for about 250 external anomalies on a pipeline that areexcavated and measured, the sizing errors generally fall within 10%tolerance. One approach indicates that independence between “true value”and “sizing error” may imply a certain amount of statistical dependencebetween “measured value” and “sizing error.” Analysis generally bearsout that, if sizing errors are independent of the true depths, thedeeper reported values (e.g., for anomalies) have a higher likelihood ofbeing overestimates of the true anomaly size—even if the overallinspection tool performance falls within the specified tolerances. Thisanalysis explains why an assessment of tool sizing error needs to occuron a statistical basis. A second feature is the effect of sizing erroron the estimation of the growth (e.g., corrosion) rate. Sizing error maycause the distribution of anomaly sizes to be wider than in reality,which also has a very significant impact on the width of the corrosionrate distribution. Analysis (e.g., via computer simulation as well asthrough comparison of historical records) shows that the measured values(e.g., of corrosion rate) cover a much larger range than the truevalues. Note that due to sizing errors, negative values (which arephysically impossible) may be recorded. Very conservative approacheshave been used in the past, often leading to frustration and perhapsexasperation at the lack of available solutions.

FIG. 9 graphically illustrates the aforementioned inaccuracy problem anda mechanism employed by certain embodiments of the IA systems to offerone solution to the problem. In particular, FIG. 9 comprises a graphic900 that comprises a horizontal axis 902 corresponding to a firstin-line inspection (ILI1) and a vertical axis 904 corresponding to asecond in-line inspection (ILI2). Unity line 906 provides a basis forunderstanding growth rates for measured anomalies, such as thoseindicated by diamonds 908. The IA system (e.g., algo compute logic 308as executed by the processor 302) cross-plots the matched anomalies fromthe first and second inspections (ILI1 and ILI2). If there is no growthand no sizing uncertainty, all data falls on the unity line 906. Datapoints above the unity line 906 indicate growth, whereas data pointsbelow the unity line 906 indicate bias between two inspections (hencerequiring validation). Due to sizing uncertainties and pair mismatches,some data falls significantly outside the range, as represented byelliptical areas 912 and 914. Accordingly, data points located withinthe elliptical area 910 are used to compute an accurate estimate of thegrowth (e.g., corrosion) rate.

In general, the IA system (e.g., via the algo compute logic 308 asexecuted by processor 302) accurately accounts for sizing uncertainty byproviding two, complementary, components in its solution. First, thereis an interpretation of individual inspection results, since asindicated above, there tends to be an overestimation bias for the deeperanomaly reports and underestimation of the shallower anomalies as wellas an underestimation for the shallower anomalies. Secondly, there is amore accurate calculation of corrosion rates. In other words, the sizingerror tends to artificially inflate the spread in the corrosion ratedistribution. The IA system filters out some of the noise and thereforeimproves the accuracy of the corrosion rates. Though described in thecontext of pipelines (e.g., as evidenced by ILI methods), the accountingfor sizing uncertainty may be employed in remaining life predictions forvarious types of fluid carrying vessels, including pressure vessels andpiping and heat exchangers 102.

A more detailed method employed by IA systems with respect to sizinguncertainty is described below and also later in more summary fashion inthe context of flow diagrams 14-16. For instance, certain embodiments ofIA systems more accurately predict the corrosion rate of a given anomalyby starting with matched pairs (e.g., sets of anomalies which are deemedto represent the same anomalies (e.g., same location) for separateinspections that may be several years apart in some instances. Each ofthe inspections comprises a specified accuracy, modeled (by the IAsystem) by a distribution. Such a model creates estimates for both themost likely values of the anomalies themselves as well as the corrosionrates of each of the anomalies. The resulting statistical corrosion ratemodel can take into consideration the dependence (or lack thereof) offuture corrosion rates on past observed rates, and the dependence (orlack thereof of future corrosion rates on the past observed anomalysizes. Accordingly, one embodiment of an IA system begins with thematched anomalies m_(1,1) through m_(1,n) at time t₁ and m_(2,1) throughm_(2,n) at time t₂ through m_(m,1) through m_(m,n) at time t_(m). The IAsystem also begins with a statistical description of the accuracy (e.g.,error “e”) of each inspection at time t₁, t₂, through t_(m). From thisbasis, the IA system computes most likely values of the set of matchedanomalies d_(1,1) through d_(1,n), at time t₁ and d_(2,1) throughd_(2,n) at time t₂ through d_(m,1) through d_(m,n) at time t_(m). Fromthe computation of the most likely values of matched anomalies, the IAsystem computes the corrosion rates for each set of matched pairs, whichgives rise to the observed values of m=d+e at each of the inspectiontimes, subject to the following constraints: (a) m=d+e (measured valueis equal to the sum of the unknown, ‘true’ value+a sizing error); (b)the corrosion rate is statistically compliant with the postulatedcorrosion models. Note that in some embodiments, one or more corrosionrate models may be postulated, including (1) future growth independentof past growth; (2) future growth conditionally dependent on past growthwith a correlation that is independent of the actual wall loss observedduring one or more inspections; (3) future growth is conditionallydependent on past growth with correlation that is itself functionallydependent on the actual wall loss observed ruing one or moreinspections. A summary of one or more of these processes is describedbelow in association with a respective flow diagram.

It should be appreciated, in the context of the present disclosure, thatone embodiment of a method to compute the likelihood of a leak, referredto as method 1000 as shown in FIG. 10 and implemented by the computingdevice 300 (e.g., via the processor 302 executing one or more softwarelogic in memory 304), includes receiving a first set of inputscomprising values associated with wall thickness, the values ascertainedthrough an inspection method for a first zone of a heat exchanger, thefirst zone comprising a plurality of inspected and uninspected tubes(1002), computing a respective estimate of a likelihood of a leakoccurring at the plurality of inspected and uninspected tubes over atime continuum based on the first set of inputs, an age of the inspectedtubes, and an accuracy of the inspection method (1004), and providing avisualization of an estimate for a total likelihood of a leak for theheat exchanger based on the estimate for the first zone (1006).

Another embodiment of a method 1100, shown in FIG. 11 and implemented bythe computing device 300 (e.g., via the processor 302 executing one ormore logic in memory 304) to estimate the likelihood of a leak, includesreceiving plural inputs, the plural inputs comprising respective valuesassociated with a maximum deterioration in tube wall thickness forplural inspected tubes of a heat exchanger, age of the plural inspectedtubes, an accuracy associated with a method to inspect the tubes, andlimit states comprising predefined risk and consequence levelsassociated with safety or a combination of safety and assets, the pluralinspected tubes distributed among plural non-overlapping zones of theheat exchanger (1102), computing an estimate of a total likelihood of aleak occurring annually in the heat exchanger at the plural inspectedtubes and uninspected tubes of the heat exchanger based on the pluralinputs (1104), and responsive to the computation, providing a userinterface that is configured to present a visualization of the estimateas compared to target likelihood index values that are based on thelimit states (1106).

Another embodiment of a method 1200, shown in FIG. 12 and implemented bythe computing device 300 (e.g., via the processor 302 executing one ormore logic stored in a computer readable medium) to estimate thelikelihood of a leak includes receiving plural inputs associated withtube wall thickness for plural inspected tubes of a heat exchanger, ageof the plural inspected tubes, an inspection method accuracy to inspectthe tubes, and limit states comprising predefined risk and consequencelevels associated with operation of the heat exchanger, the pluralinspected tubes distributed among plural non-overlapping zones of theheat exchanger (1202), computing an estimate of a total likelihood of aleak occurring annually in the heat exchanger at the plural inspectedtubes and uninspected tubes of the heat exchanger based on the pluralinputs (1204), and providing a simultaneous visualization of theestimate and target likelihood index values that are based on the limitstates (1206).

Another embodiment of a method 1300, shown in FIG. 13 and implemented bythe computing device 300 (e.g., via the processor 302 executing one ormore logic stored in a computer readable medium) to estimate thelikelihood of a leak. The method 1300 starts with an initial collectionof data based on inspection reports, comprising a set of anomalies basedon inspection of tubes in one more zones of a heat exchanger 103, a setof best estimates for time-averaged growth (e.g., corrosion, etc.) ratesfor each anomaly, and an overall statistical distribution of growthrates. Then, for each time increment, the method 1300 computes anestimate of the likelihood of a leak for any given tube by accountingfor variability in tube wall thickness (1302), correlating future growthto past historical growth rates (1304). For instance, the historicalgrowth rates may be stored in the database 312 of the storage device310, among other storage locales. Further, it is noted that the amountof uncertainty on future growth rates depends on the time lag. Forinstance, the further out into the future a prediction is made, thelesser the correlation between past and future growth rates is likely tobe. Though performable via standard Monte Carlo simulations, it shouldbe understood that advanced analytical approaches may be employed toimprove accuracy and performance. The method 1300 further comprises, foreach zone, operating on an assumption that un-inspected tubes exhibitwall loss similar to wall loss distribution of inspected tubes (1306).For instance, the wall loss distribution is derived from the inspectedtubes is assumed to also apply to the uninspected tubes of a given zone.The method 1300 continues by providing an estimate of a likelihood of aleak in any given tube. It is noted that in some embodiments, andparticularly for large systems, only the contribution from the mostimportant tubes is explicitly computed, which enables a significantreduction in computer time while maintaining sufficient accuracy.

Another embodiment of a method 1400, shown in FIG. 14 and implemented bythe computing device 300 (e.g., via the processor 302 executing one ormore logic stored in memory 304) to account for sizing uncertaintyincludes receiving plural sets of values corresponding to plural matchedpairs of anomalies from a first inspection and second inspectionfollowing the first inspection, a first portion of each paircorresponding to the first inspection and a second portion of each paircorresponding to the second inspection, the plural sets of valuescorresponding to wall loss information for plural locations of a fluidcarrying vessel (1402), computing first and second statisticaldescriptions of a respective accuracy of the first and secondinspections (1404), and computing a revised estimate of the plural setsof values based on the first and second statistical descriptions (1406).

Another embodiment of a method 1500, shown in FIG. 15 and implemented bythe computing device 300 (e.g., via the processor 302 executing one ormore logic stored in memory 304) to account for sizing uncertaintyincludes receiving plural sets of values corresponding to plural matchedpairs of anomalies from a first inspection and second inspectionfollowing the first inspection, a first portion of each paircorresponding to the first inspection and a second portion of each paircorresponding to the second inspection, the plural sets of valuescorresponding to wall loss information for plural locations of a fluidcarrying vessel (1502), computing first and second statisticaldescriptions of a respective accuracy of the first and secondinspections (1504), computing a revised estimate of the plural sets ofvalues based on the first and second statistical descriptions (1506),and computing corrosion rates for the anomalies of each matched pairbased on the revised estimate (1508).

Another embodiment of a method 1600, shown in FIG. 16 and implemented bythe computing device 300 (e.g., via the processor 302 executing one ormore logic stored in memory 304) to account for sizing uncertaintyincludes receiving plural sets of values corresponding to plural matchedsets of anomalies from plural inspections separated in time, each setcomprising plural portions, each portion corresponding to a respectiveinspection, the plural sets of values corresponding to wall lossinformation for plural locations of a fluid carrying vessel (1602),computing statistical descriptions of a respective accuracy for each ofthe plural inspections (1604), computing a revised estimate for each ofthe plural sets of values based on the statistical descriptions (1606),and computing growth rates for the anomalies of each of the matched setsbased on the revised estimate (1608).

Any software components illustrated herein are abstractions chosen toillustrate how functionality is partitioned among components in someembodiments of the IA systems disclosed herein. Other divisions offunctionality are also possible, and these other possibilities areintended to be within the scope of this disclosure.

Any software components included herein are described in terms of codeand data, rather than with reference to a particular hardware deviceexecuting that code. Furthermore, to the extent that systems and methodsare described in object-oriented terms, there is no requirement that thesystems and methods be implemented in an object-oriented language.Rather, the systems and methods can be implemented in any programminglanguage, and executed on any hardware platform.

Any software components referred to herein include executable code thatis packaged, for example, as a standalone executable file, a library, ashared library, a loadable module, a driver, or an assembly, as well asinterpreted code that is packaged, for example, as a class. In general,the components used by the systems and methods of reducing media streamdelay are described herein in terms of code and data, rather than withreference to a particular hardware device executing that code.Furthermore, the systems and methods can be implemented in anyprogramming language, and executed on any hardware platform.

The flow diagrams herein provide examples of the operation of the IAsystems and methods. Blocks in these diagrams represent procedures,functions, modules, or portions of code which include one or moreexecutable instructions for implementing logical functions or steps inthe process. Alternate implementations are also included within thescope of the disclosure. In these alternate implementations, functionsmay be executed out of order from that shown or discussed, includingsubstantially concurrently or in reverse order, depending on thefunctionality involved.

The foregoing description of illustrated embodiments of the presentdisclosure, including what is described in the abstract, is not intendedto be exhaustive or to limit the disclosure to the precise formsdisclosed herein. While specific embodiments of, and examples for, thedisclosure are described herein for illustrative purposes only, variousequivalent modifications are possible within the spirit and scope of thepresent disclosure, as those skilled in the relevant art will recognizeand appreciate. As indicated, these modifications may be made to thepresent disclosure in light of the foregoing description of illustratedembodiments.

Thus, while the present disclosure has been described herein withreference to particular embodiments thereof, a latitude of modification,various changes and substitutions are intended in the foregoingdisclosures, and it will be appreciated that in some instances somefeatures of embodiments of the disclosure will be employed without acorresponding use of other features without departing from the scope ofthe disclosure. Therefore, many modifications may be made to adapt aparticular situation or material to the essential scope of the presentdisclosure. It is intended that the disclosure not be limited to theparticular terms used in following claims and/or to the particularembodiment disclosed as the best mode contemplated for carrying out thisdisclosure, but that the disclosure will include any and all embodimentsand equivalents falling within the scope of the appended claims.

1. A method, comprising: receiving plural sets of values correspondingto plural matched pairs of anomalies from a first inspection and secondinspection following the first inspection, a first portion of each paircorresponding to the first inspection and a second portion of each paircorresponding to the second inspection, the plural sets of valuescorresponding to wall loss information for plural locations of a fluidcarrying vessel; computing by a processor first and second statisticaldescriptions of a respective accuracy of the first and secondinspections; and computing by the processor a revised estimate of theplural sets of values based on the first and second statisticaldescriptions.
 2. The method of claim 1, further comprising computing bythe processor growth rates for the anomalies of each matched pair basedon the revised estimate.
 3. The method of claim 2, wherein thecomputation of the growth rates is based on historical measured growthrates.
 4. The method of claim 2, wherein the computation of the growthrates is conditionally based on historical measured growth rates.
 5. Themethod of claim 2, wherein the computation of the growth rates is basedon historical measured anomaly sizes.
 6. The method of claim 2, whereinthe growth rate comprises a corrosion rate.
 7. The method of claim 2,wherein the anomalies comprise a measured maximum wall thicknessdeterioration.
 8. The method of claim 1, further comprising identifyingthe plural matched pairs of anomalies.
 9. A system, comprising: a memorywith logic; and a processor configured to execute the logic to: receiveplural sets of values corresponding to plural matched pairs of anomaliesfrom a first inspection and second inspection following the firstinspection, a first portion of each pair corresponding to the firstinspection and a second portion of each pair corresponding to the secondinspection, the plural sets of values corresponding to wall lossinformation for plural locations of a fluid carrying vessel; computefirst and second statistical descriptions of a respective accuracy ofthe first and second inspections; compute a revised estimate of theplural sets of values based on the first and second statisticaldescriptions; and compute corrosion rates for the anomalies of eachmatched pair based on the revised estimate.
 10. The system of claim 9,wherein the computation of the corrosion rates is based on historicalmeasured corrosion rates.
 11. The system of claim 9, wherein thecomputation of the corrosion rates is conditionally based on historicalmeasured corrosion rates.
 12. The system of claim 9, wherein thecomputation of the corrosion rates is based on historical measuredanomaly sizes.
 13. The system of claim 9, wherein the anomalies comprisea measured maximum wall thickness corrosion.
 14. The system of claim 9,wherein the processor is further configured with the logic to identifythe plural matched pairs of anomalies.
 15. A computer readable mediumencoded with software code that is executed by a processor to cause theprocessor to: receive plural sets of values corresponding to pluralmatched sets of anomalies from plural inspections separated in time,each set comprising plural portions, each portion corresponding to arespective inspection, the plural sets of values corresponding to wallloss information for plural locations of a fluid carrying vessel;compute statistical descriptions of a respective accuracy for each ofthe plural inspections; compute a revised estimate for each of theplural sets of values based on the statistical descriptions; and computegrowth rates for the anomalies of each of the matched sets based on therevised estimate.
 16. The computer readable medium of claim 15, whereinthe computation of the growth rates is based on historical measuredcorrosion rates.
 17. The computer readable medium of claim 15, whereinthe computation of the growth rates is conditionally based on historicalmeasured corrosion rates.
 18. The computer readable medium of claim 15,wherein the computation of the growth rates is based on historicalmeasured anomaly sizes.
 19. The computer readable medium of claim 15,wherein the anomalies comprise a measured maximum wall thickness growth.20. The computer readable medium of claim 15, wherein the processor isfurther configured with the software code to identify the anomalies ofeach set that match.